Transitions in a PhD in Nursing Program: A Critical Reflection on Students' Perceptions
Bibliographic record
Abstract
Introduction: The purpose of this critical reflection is to share the collective experiences of eight doctoral students transitioning through a PhD nursing program at a Canadian University. Background: Globally, a nursing shortage of over a million nurses threatens to significantly impact patient safety and quality of care. One proposed response to the nursing shortage is to increase enrolment of students in nursing programs, with the aim of graduating more qualified nurses. However, a concurrent shortage of nursing faculty is impacting the profession’s ability to effectively respond to this issue, with fewer nurses completing doctorate degrees than there are existing vacant faculty positions. We propose that one solution to address the nursing faculty shortage, and to ensuring that nursing can respond to the ever-increasing complexity of patient care, is to improve the doctoral student transition process. Method: We used critical reflection to explore our experiences of transitioning into a PhD in Nursing program. Virtual group meetings via an online conferencing platform were conducted using a semi-structured interview format. Using the Bridges Transition Model (BTM) as a theoretical framework, we organized our reflections using the three phases from the BTM framework: ending, neutral zone, and new beginning. As this was a critical reflection, where all participants are also listed as authors, formal ethics approval was not required. Discussion: The ending phase symbolized the transition into the PhD program while still maintaining former professional roles and was characterized by a sense of loss of identity. Moving from the ending phase into the neutral zone phase required realignment of priorities, a shift in self-identity, and recognizing what facilitators are needed to transition. When navigating the neutral zone, we considered new roles and relationships and how they could provide support during this phase. Our transition from the neutral zone to the new beginnings phase extended beyond the PhD program to include the transition to life after the PhD. While some looked forward with hope and anticipation of the new beginning phase, others highlighted the uncertainties of post-PhD life. We identified community building and career mentoring as two strategies that might ease transitions and help PhD students with degree completion. Conclusions: This paper contributes to the literature on doctoral students’ experiences as they transition through a PhD in Nursing program. We recommend that nursing faculties incorporate strategies such as career coaching and formal supports for the development of student-led communities of practice. Helping PhD students navigate transitions associated with completion of the degree may reduce attrition and increase the potential supply of tenure-track nursing faculty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".